DocumentCode :
2694887
Title :
Differential evolution with adaptive parameter setting for multi-objective optimization
Author :
Zielinski, Karin ; Laur, Rainer
Author_Institution :
Univ. of Bremen, Bremen
fYear :
2007
fDate :
25-28 Sept. 2007
Firstpage :
3585
Lastpage :
3592
Abstract :
Control parameter settings influence the convergence probability and convergence speed of evolutionary algorithms but it is often not obvious how to choose them. In this work an adaptive approach for setting the control parameters of a multi-objective differential evolution algorithm is presented. The adaptive approach is based on methods from design of experiments, so it is able to detect significant performance differences of individual parameters as well as interaction effects between parameters. It is evaluated based on 13 test functions and several performance measures.
Keywords :
convergence; design of experiments; evolutionary computation; probability; control parameter settings; convergence probability; convergence speed; design of experiments; differential evolution algorithm; multiobjective optimization; Adaptive control; Convergence; Design methodology; Evolutionary computation; Programmable control; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
Type :
conf
DOI :
10.1109/CEC.2007.4424937
Filename :
4424937
Link To Document :
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